Similarly,those who already carry an informational“virus”can still benefitfrom inoculation treatments and become less susceptible to futurepersuasion and deception attempts.
Perhaps this treatment can help a whole range of people who might already be susceptible to believing misinformation.
by exposing peopleto a weakened version of a misleading argument, and by pre-emptively refuting this argument, attitudinal resistance can be con-ferred against future deception attempts.
Wouldn't it depend on the type of misinformation? I don't see why exposing someone to one type of misinformation and enabling them to refute it would help with other misinformation.
Accordingly, developing better debunkingand fact-checking tools is therefore unlikely to be sufficient tostem theflow of online misinformation
Simply flagging information is not enough to ensure that people don't view a piece of misinformation. People also do not have the time and motivation to seek out third-party fact checking sources while on social media.
For example, recentestimates suggest that about 47 million Twitter accounts (~15%)are bots (Varol et al.,2017). Some of these bots are used to spreadpolitical misinformation, especially during election campaigns.
Why haven't social media platorms invested more time and effort into eliminating some of these bots? If they are causing major issues in the spread of misinformation, why hasn't this issue been addressed?
In the game, players take on the role of a fake newsproducer and learn to master six documented techniques commonly used in the productionof misinformation: polarisation, invoking emotions, spreading conspiracy theories, trollingpeople online, deflecting blame, and impersonating fake accounts
Becoming an expert in a task can help one pick up on patterns within that domain. If you are quickly able to recognize patterns and can associate those patterns with your expertise of creating misinformation, you can quickly realize that the information you are seeing is misinformation.
But titles and rich metadata take us only so far.
It's important to keep in mind that while a data set can lead us to some very clear conclusions, it can lead us to probable, but not guaranteed conclusions as well. In this second case, one must be careful about jumping immediately to a definitive conclusion. Often times, we see patterns in data that correlate with a specific conclusion, but we must be careful not to confuse correlation with causation.
n the ca��Qf!�!��-Am�t!��DJi.f.ti()n, .�:b:�!e. t4�_r_ejs_aJ!tg9xJ�9-JgE �����!E_��Q�:tld .of intolerance for Irish th�1ll�s, tli�\!itl�s ofwork§.carry a spe�ial we�ght, for i!_is !11 the title that an author first Ill��t§_ his readers. To wllatexJent, th_�n, _do_ l.It�h: , American autgQJJ,_ tc.l�:riJ:tfyJhemselv�s-as Irish in the titles of their books?
I think this a great example of reading between the lines of the data. Most people would likely only see a long list of titles from Irish American authors. However, when we incorporate background information into these sets of data, we can then categorize the titles based upon key words to pull out certain conclusions from the text.
If we look only to the dotted ( eastern) line, then figure 5.2 confirms Fanning's further observation that Irish American fiction flourished at the turn of the century and then again in the 1960s and '70s, when cultural changes made writing along ethnic lines more popular and appealing. The western line, however, tells a different story
Before reaching a conclusion about a particular set of data, one must make sure that the entirety of the data set agrees with the claim. Fanning's assertion might be correct in a particular case, specifically within eastern Irish American writers, but it doesn't seem to apply to everyone else, specifically the western Irish American writers.
this is not a huge corpus.
What works are not within this database and why? How did the works that are included within this database make it in? Was it due by mere chance that these Irish American literature records are within this database or is there some kind of criteria that these particular pieces met?
}2etermining how. and whether a W?rk got inclt1ded i.n the database w�s someti1nes a subjective p�()C_���_:
It is important to note the possible ambiguities of certain categorizations as they might differentiate your own data from someone else's data. In this example, if you categorized Kathleen as Californian, but someone else categorized her as a New Yorker, your own conclusion and the conclusion of your peer might be slightly different, even though it's pulled from the same data set.
indicating where each author was from and where each text was set were also added to the records.
Here we see a sharp contrast between two different data sets. The first one described is somewhat messy and does not contain metadata to help organize and classify the data, whereas the second data set is much more organized and likely easier to work with.